# load dataset loader = DataLoader(dataset, sampleEach) print("Decoding " + str(loader.getNumSamples()) + " samples now.") print("") # metrics calculates CER and WER for dataset m = Metrics(loader.lm.getWordChars()) # write results to csv csv = Utils.CSVWriter() # decode each sample from dataset for (idx, data) in enumerate(loader): # decode matrix res = wordBeamSearch(data.mat, 10, loader.lm, useNGrams) print("Sample: " + str(idx + 1)) print("Filenames: " + data.fn) print('Result: "' + res + '"') print('Ground Truth: "' + data.gt + '"') strEditDist = str(editdistance.eval(res, data.gt)) print("Editdistance: " + strEditDist) # output CER and WER m.addSample(data.gt, res) print("Accumulated CER and WER so far:", "CER:", m.getCER(), "WER:", m.getWER()) print("") # output to csv csv.write([res, data.gt, strEditDist])
# load dataset loader = DataLoader(dataset, sampleEach) print('Decoding ' + str(loader.getNumSamples()) + ' samples now.') print('') # metrics calculates CER and WER for dataset m = Metrics(loader.lm.getWordChars()) # write results to csv csv = Utils.CSVWriter() # decode each sample from dataset for (idx, data) in enumerate(loader): # decode matrix res = wordBeamSearch(data.mat, 10, loader.lm, useNGrams) print('Sample: ' + str(idx + 1)) print('Filenames: ' + data.fn) print('Result: "' + res + '"') print('Ground Truth: "' + data.gt + '"') strEditDist = str(editdistance.eval(res, data.gt)) print('Editdistance: ' + strEditDist) # output CER and WER m.addSample(data.gt, res) print('Accumulated CER and WER so far:', 'CER:', m.getCER(), 'WER:', m.getWER()) print('') # output to csv csv.write([res, data.gt, strEditDist])